Fetching the paper…
Reading the bibliography…
Despite its popularity, several recent works question the effectiveness of MAML when test tasks are different from training tasks, thus suggesting various task-conditioned methodology to improve the initialization.
Evolutionary principles in self-referential learning. on learning now to learn: The meta-meta-meta…-hook
J. Schmidhuber · 1987
Earlier work this paper cites.
On the optimization of a synaptic learning rule
S. Bengio, Y. Bengio, J. Cloutier, and J. Gecsei · 1992
Earlier work this paper cites.
Learning to control fast-weight memories: An alternative to dynamic recurrent networks
J. Schmidhuber · 1992
Earlier work this paper cites.
Learning to learn using gradient descent
S. Hochreiter, A. Younger, and P. Conwell · 2001
Earlier work this paper cites.
Adaptive subgradient methods for online learning and stochastic optimization
J. Duchi, E. Hazan, and Y. Singer · 2011
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Earlier work this paper cites.
Learning to learn
S. Thrun and L. Pratt · 2012
Earlier work this paper cites.
Adadelta: an adaptive learning rate method
M. D. Zeiler · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
G. Koch, R. Zemel, and R. Salakhutdinov · 2015
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. Gómez, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas · 2016
Earlier work this paper cites.
Meta-learning with memory-augmented neural networks
A. Santoro, S. Bartunov, M. Botvinick, D. Wierstra, and T. Lillicrap · 2016
Earlier work this paper cites.
Matching networks for one shot learning
O. Vinyals, C. Blundell, T. Lillicrap, k. kavukcuoglu, and D. Wierstra · 2016
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
Earlier work this paper cites.
Meta-sgd: Learning to learn quickly for few shot learning
Z. Li, F. Zhou, F. Chen, and H. Li · 2017
Earlier work this paper cites.
Meta networks
T. Munkhdalai and H. Yu · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
J. Snell, K. Swersky, and R. Zemel · 2017
Cited alongside, same era.
Probabilistic model-agnostic meta-learning
C. Finn, K. Xu, and S. Levine · 2018
Cited alongside, same era.
Recasting gradient-based meta-learning as hierarchical bayes
E. Grant, C. Finn, S. Levine, T. Darrell, and T. Griffiths · 2018
Cited alongside, same era.
Gradient-based meta-learning with learned layerwise metric and subspace
Y. Lee and S. Choi · 2018
Cited alongside, same era.
Rapid adaptation with conditionally shifted neurons
Learning to learn with conditional class dependencies
X. Jiang, M. Havaei, F. Varno, G. Chartrand, N. Chapados, and S. Matwin · 2019
Later among the works it cites.
Adaptive gradient-based meta-learning methods
M. Khodak, M.-F. F. Balcan, and A. S. Talwalkar · 2019
Later among the works it cites.
Meta-learning with differentiable convex optimization
K. Lee, S. Maji, A. Ravichandran, and S. Soatto · 2019
Later among the works it cites.
Meta-curvature
E. Park and J. B. Oliva · 2019
Later among the works it cites.
Meta-learning with implicit gradients
A. Rajeswaran, C. Finn, S. Kakade, and S. Levine · 2019
Later among the works it cites.
Meta-learning with latent embedding optimization
A. A. Rusu, D. Rao, J. Sygnowski, O. Vinyals, R. Pascanu, S. Osindero, and R. Hadsell · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Munkhdalai, X. Yuan, S. Mehri, and A. Trischler · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
A. Nichol, J. Achiam, and J. Schulman · 2018
Cited alongside, same era.
Tadam: Task dependent adaptive metric for improved few-shot learning
B. N. Oreshkin, P. Rodriguez, and A. Lacoste · 2018
Cited alongside, same era.
Meta-learning for semi-supervised few-shot classification
M. Ren, E. Triantafillou, S. Ravi, J. Snell, K. Swersky, J. B. Tenenbaum, H. Larochelle, and R. S. Zemel · 2018
Cited alongside, same era.
Learning to compare: Relation network for few-shot learning
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. Torr, and T. M. Hospedales · 2018
Cited alongside, same era.
How to train your maml
A. Antoniou, H. Edwards, and A. Storkey · 2019
Cited alongside, same era.
Multimodal model-agnostic meta-learning via task-aware modulation
R. Vuorio, S.-H. Sun, H. Hu, and J. J. Lim · 2019
Later among the works it cites.
Hierarchically structured meta-learning
H. Yao, Y. Wei, J. Huang, and Z. Li · 2019
Later among the works it cites.
Fast context adaptation via meta-learning
L. M. Zintgraf, K. Shiarlis, V. Kurin, K. Hofmann, and S. Whiteson · 2019
Later among the works it cites.
Learning to forget for meta-learning
S. Baik, S. Hong, and K. M. Lee · 2020
Closest in time.
Meta-learning with warped gradient descent
S. Flennerhag, A. A. Rusu, R. Pascanu, F. Visin, H. Yin, and R. Hadsell · 2020
Closest in time.
Rapid learning or feature reuse? towards understanding the effectiveness of maml
A. Raghu, M. Raghu, S. Bengio, and O. Vinyals · 2020
Closest in time.
Meta-dataset: A dataset of datasets for learning to learn from few examples
E. Triantafillou, T. Zhu, V. Dumoulin, P. Lamblin, K. Xu, R. Goroshin, C. Gelada, K. Swersky, P.-A. Manzagol, and H. Larochelle · 2020
Closest in time.
Meta-learning without memorization
M. Yin, G. Tucker, M. Zhou, S. Levine, and C. Finn · 2020
Closest in time.